# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='CascadeEncoderDecoder',
    num_stages=2,
    pretrained='open-mmlab://msra/hrnetv2_w18',
    backbone=dict(type='HRNet',
                  norm_cfg=norm_cfg,
                  norm_eval=False,
                  extra=dict(stage1=dict(num_modules=1,
                                         num_branches=1,
                                         block='BOTTLENECK',
                                         num_blocks=(4, ),
                                         num_channels=(64, )),
                             stage2=dict(num_modules=1,
                                         num_branches=2,
                                         block='BASIC',
                                         num_blocks=(4, 4),
                                         num_channels=(18, 36)),
                             stage3=dict(num_modules=4,
                                         num_branches=3,
                                         block='BASIC',
                                         num_blocks=(4, 4, 4),
                                         num_channels=(18, 36, 72)),
                             stage4=dict(num_modules=3,
                                         num_branches=4,
                                         block='BASIC',
                                         num_blocks=(4, 4, 4, 4),
                                         num_channels=(18, 36, 72, 144)))),
    decode_head=[
        dict(type='FCNHead',
             in_channels=[18, 36, 72, 144],
             channels=sum([18, 36, 72, 144]),
             in_index=(0, 1, 2, 3),
             input_transform='resize_concat',
             kernel_size=1,
             num_convs=1,
             concat_input=False,
             dropout_ratio=-1,
             num_classes=19,
             norm_cfg=norm_cfg,
             align_corners=False,
             loss_decode=dict(type='CrossEntropyLoss',
                              use_sigmoid=False,
                              loss_weight=0.4)),
        dict(type='OCRHead',
             in_channels=[18, 36, 72, 144],
             in_index=(0, 1, 2, 3),
             input_transform='resize_concat',
             channels=512,
             ocr_channels=256,
             dropout_ratio=-1,
             num_classes=19,
             norm_cfg=norm_cfg,
             align_corners=False,
             loss_decode=dict(type='CrossEntropyLoss',
                              use_sigmoid=False,
                              loss_weight=1.0)),
    ],
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
